Structural variants (SVs) are major contributors to genome diversity and disease susceptibility, particularly in cancer. Although third-generation sequencing technologies have substantially improved SV detection sensitivity, accurate detection of complex SVs remains challenging due to fragmented and heterogeneous alignment signals, as well as the dependence of many existing methods on predefined variant models. In this paper, we propose gSV, a general SV detector that integrates alignment-based and assembly-based approaches with the maximum exact match strategy, with particular emphasis on resolving SVs with complex or atypical alignment signatures. Without predefined assumptions about SV types, gSV captures diverse variant signals, enabling the detection of SVs that are usually missed by conventional tools. Benchmarking using both simulated datasets and real long-read sequencing data demonstrates that gSV achieves improved sensitivity and overall detection performance compared with current state-of-the-art SV callers, particularly for simple and complex SV events with complex alignment patterns. Unique SV discoveries in four breast cancer cell lines, particularly in cancer-associated genes, demonstrate the potential biological relevance of gSV-enabled discoveries. Furthermore, analysis of a breast cancer cohort from the Chinese population highlights the utility of gSV for population-scale genomic studies. Collectively, gSV provides a unified framework for comprehensive SV discovery in both research and clinical genomics settings.

Summary Multi‐organ segmentation is a critical prerequisite for many clinical applications. Deep learning‐based approaches have recently achieved promising results on this task. However, they heavily rely on massive data with multi‐organ annotated, which is labor‐ and expert‐intensive and thus difficult to obtain. In contrast, single‐organ datasets are easier to acquire, and many well‐annotated ones are publicly available. It leads to the partially labeled issue: How to learn a unified multi‐organ segmentation model from several single‐organ datasets? Pseudo‐label‐based methods and conditional information‐based methods make up the majority of existing solutions, where the former largely depends on the accuracy of pseudo‐labels, and the latter has a limited capacity for task‐related features. In this paper, we propose the Conditional Dynamic Attention Network (CDANet). Our approach is designed with two key components: (1) multisource parameter generator, fusing the conditional and multiscale information to better distinguish among different tasks, and (2) dynamic attention module, promoting more attention to task‐related features. We have conducted extensive experiments on seven partially labeled challenging datasets. The results show that our method achieved competitive results compared with the advanced approaches, with an average Dice score of 75.08%. Additionally, the Hausdorff Distance is 26.31, which is a competitive result.

Few-shot learning can potentially learn the target knowledge in extremely few data regimes. Existing few-shot medical image segmentation methods fail to consider the global anatomy correlation between the support and query sets. They generally adopt a weak one-way information transmission that can not fully explore the knowledge to segment query data. To address this problem, we propose a novel Symmetrical Supervision network based on traditional two-branch methods. We raise two main contributions: (1) The Symmetrical Supervision Mechanism is leveraged to strengthen the supervision of network training; (2) A transformer-based Global Feature Alignment module is introduced to increase the global consistency between the two branches. Experimental results on two challenging datasets (abdominal segmentation dataset CHAOS and cardiac segmentation dataset MS-CMRSeg) show a remarkable performance compared to other comparing methods.

Retinal vessel segmentation is a critical procedure towards the accurate visualization, diagnosis, early treatment, and surgery planning of ocular diseases. Recent deep learning-based approaches have achieved impressive performance in retinal vessel segmentation. However, they usually apply global image pre-processing and take the whole retinal images as input during network training, which have two drawbacks for accurate retinal vessel segmentation. First, these methods lack the utilization of the local patch information. Second, they overlook the geometric constraint that retina only occurs in a specific area within the whole image or the extracted patch. As a consequence, these global-based methods suffer in handling details, such as recognizing the small thin vessels, discriminating the optic disk, etc. To address these drawbacks, this study proposes a Global and Local enhanced residual U-nEt (GLUE) for accurate retinal vessel segmentation, which benefits from both the globally and locally enhanced information inside the retinal region. Experimental results on two benchmark datasets demonstrate the effectiveness of the proposed method, which consistently improves the segmentation accuracy over a conventional U-Net and achieves competitive performance compared to the state-of-the-art.

Summary According to statistics, kidney cancer is one of the most deadly cancer. An early and accurate diagnosis can significantly increase the cure rate. Accurate segmentation of kidney tumors in CT images plays an important role in kidney cancer diagnosis. However, it is a challenging task due to many different aspects, such as low contrast, irregular motion, diverse shapes, and sizes. For solving this issue, we proposed a SE‐R esNeXT U ‐Net (SERU) model in this study, which takes the advantages of SE‐Net, ResNeXT and U‐Net. Besides, we implement our model in a coarse‐to‐fine manner to utilize the information of context and key slices from the left and right kidney. We train and test our method on the KiTS19 Challenge. Experimental results demonstrate that our model can achieve promising results.

Person re-identification (re-ID) is a challenging instance retrieval problem, especially when identity annotations are not available for training. Although modern deep re-ID approaches have achieved great improvement, it is still difficult to optimize the deep re-ID model and learn discriminative person representation without annotations in training data. To address this challenge, this study considers the problem of unsupervised person re-ID and introduces a novel approach to solve this problem by leveraging virtual and real data. Our approach includes two components: virtual person generation and training of the deep re-ID model. For virtual person generation, we learn a person generation model and a camera style transfer model using unlabeled real data to generate virtual persons with different poses and camera styles. The virtual data is formed as labeled training data, enabling subsequent training deep re-ID model in supervision. For training of the deep re-ID model, we divide it into three steps: 1) pre-training a coarse re-ID model by using virtual data; 2) collaborative filtering based positive pair mining from the real data; and 3) fine-tuning of the coarse re-ID model by leveraging the mined positive pairs and virtual data. The final re-ID model is achieved by iterating between step 2 and step 3 until convergence. Extensive experiments demonstrate the effectiveness of our method. Experimental results on two large-scale datasets, Market-1501 and DukeMTMC-reID, show the advantages of our method over state-of-the-art approaches in unsupervised person re-ID. Our code is now available online1.

Anchor-based deep methods are the most widely used methods for face detection and have reached the state-of-the-art result. Compared with anchor-based methods that estimates the bounding-box rely on some pre-defined anchor boxes, anchor-free methods perform the localization by predicting the offsets of a pixel inside a face to its outside boundaries whose accuracies are much more precise. However, anchor-free methods suffer the drawback of low recall-rate mainly because 1) only using single scale features lead to miss detection of small faces, 2) the highly intra-class imbalance problem among different size faces. In this paper, to address these problems, we propose a unified anchor-free network for detecting multi-scale faces by leveraging the local and global contextual information of multi-layer features. We also utilize a scale aware sampling strategy to mitigate the intra-class imbalance issue which can adaptivity select the positive samples. Furthermore, a revised focal loss function is adopted to deal with the foreground/background imbalance issue. Experimental results on two benchmark datasets demonstrate the effective of our proposed method.